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Modern machine learning algorithms like Support Vector Machine, Random Forest and Neural Nets are producing very promising results on large scale datasets. However understanding what is happening inside the black box and the nuanced differences of various implementations can be challenging. Topics and presentations will include open source tools, real-world case studies and an occasional talk by a theorist.